Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/10942#discussion_r51685919
--- Diff:
sql/hive/src/test/scala/org/apache/spark/sql/sources/BucketedReadSuite.scala ---
@@ -59,6 +61,153 @@ class BucketedReadSuite extends QueryTest with
SQLTestUtils with TestHiveSinglet
}
}
+ // To verify if the bucket pruning works, this function checks two
conditions:
+ // 1) Check if the pruned buckets (before filtering) are empty.
+ // 2) Verify the final result is the same as the expected one
+ private def checkPrunedAnswers(
+ bucketSpec: BucketSpec,
+ bucketValues: Seq[Integer],
+ filterCondition: Column,
+ originalDataFrame: DataFrame): Unit = {
+
+ val bucketedDataFrame =
hiveContext.table("bucketed_table").select("i", "j", "k")
+ val BucketSpec(numBuckets, bucketColumnNames, _) = bucketSpec
+ // Limit: bucket pruning only works when the bucket column has one and
only one column
+ assert(bucketColumnNames.length == 1)
+ val bucketColumnIndex =
bucketedDataFrame.schema.fieldIndex(bucketColumnNames.head)
+ val bucketColumn =
bucketedDataFrame.schema.toAttributes(bucketColumnIndex)
+ val matchedBuckets = new BitSet(numBuckets)
+ bucketValues.foreach { value =>
+ matchedBuckets.set(DataSourceStrategy.getBucketId(bucketColumn,
numBuckets, value))
+ }
+
+ // Filter could hide the bug in bucket pruning. Thus, skipping all the
filters
+ val rdd =
bucketedDataFrame.filter(filterCondition).queryExecution.executedPlan
+ .find(_.isInstanceOf[PhysicalRDD])
+ assert(rdd.isDefined)
+
+ val checkedResult = rdd.get.execute().mapPartitionsWithIndex { case
(index, iter) =>
+ if (matchedBuckets.get(index % numBuckets)) Iterator(true) else
Iterator(iter.isEmpty)
+ }
+ // checking if all the pruned buckets are empty
+ assert(checkedResult.collect().forall(_ == true))
+
+ checkAnswer(
+ bucketedDataFrame.filter(filterCondition).orderBy("i", "j", "k"),
+ originalDataFrame.filter(filterCondition).orderBy("i", "j", "k"))
+ }
+
+ test("read partitioning bucketed tables with bucket pruning filters") {
+ withTable("bucketed_table") {
+ val numBuckets = 8
+ val bucketSpec = BucketSpec(numBuckets, Seq("j"), Nil)
+ // json does not support predicate push-down, and thus json is used
here
+ df.write
+ .format("json")
+ .partitionBy("i")
+ .bucketBy(numBuckets, "j")
+ .saveAsTable("bucketed_table")
+
+ for (j <- 0 until 13) {
+ // Case 1: EqualTo
+ checkPrunedAnswers(
+ bucketSpec,
+ bucketValues = j :: Nil,
+ filterCondition = $"j" === j,
+ df)
+
+ // Case 2: EqualNullSafe
+ checkPrunedAnswers(
+ bucketSpec,
+ bucketValues = j :: Nil,
+ filterCondition = $"j" <=> j,
+ df)
+
+ // Case 3: In
+ checkPrunedAnswers(
+ bucketSpec,
+ bucketValues = Seq(j, j + 1, j + 2, j + 3),
+ filterCondition = $"j".isin(j, j + 1, j + 2, j + 3),
+ df)
+ }
+ }
+ }
+
+ test("read non-partitioning bucketed tables with bucket pruning
filters") {
+ withTable("bucketed_table") {
+ val numBuckets = 8
+ val bucketSpec = BucketSpec(numBuckets, Seq("j"), Nil)
+ // json does not support predicate push-down, and thus json is used
here
+ df.write
+ .format("json")
+ .bucketBy(numBuckets, "j")
+ .saveAsTable("bucketed_table")
+
+ for (j <- 0 until 13) {
+ checkPrunedAnswers(
+ bucketSpec,
+ bucketValues = j :: Nil,
+ filterCondition = $"j" === j,
+ df)
+ }
+ }
+ }
+
+ test("read partitioning bucketed tables having null in bucketing key") {
+ withTable("bucketed_table") {
+ val numBuckets = 8
+ val bucketSpec = BucketSpec(numBuckets, Seq("j"), Nil)
+ val nullDF = Seq((1, "str", 3), (1, null, 5), (1, "test",
5)).toDF("i", "j", "k")
--- End diff --
Sure, will do it. Thank you!
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